Shift and gray scale invariant features for palmprint identification using complex directional wavelet and local binary pattern

  • Authors:

  • Meiru Mu;Qiuqi Ruan;Song Guo

  • Affiliations:

  • Institute of Information Science, Beijing Jiaotong University, Beijing 100044, China and Beijing Key Laboratory of Advanced Information Science and Network Technology, Beijing 100044, China;Institute of Information Science, Beijing Jiaotong University, Beijing 100044, China and Beijing Key Laboratory of Advanced Information Science and Network Technology, Beijing 100044, China;Institute of Information Science, Beijing Jiaotong University, Beijing 100044, China and Beijing Key Laboratory of Advanced Information Science and Network Technology, Beijing 100044, China

  • Venue:
  • Neurocomputing
  • Year:
  • 2011

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Abstract

In this paper, a novel feature extraction framework is presented for palmprint identification, which provides a shiftable and gray scale invariant description of image achieving high identification accuracy at a low computational cost. The image is firstly decomposed by the shiftable complex directional filter bank (CDFB) transform which provides a two-dimensional (2-D) decomposition of energy shiftable and scalable multiresolution, arbitrarily directional resolution, low redundant ratio, and efficient implementation. Further, the subband coefficients of CDFB decomposition are operated by the uniform local binary pattern (LBP) which is gray scale invariant and contains information about the distribution of the local micro-patterns. The resulting LBP mappings are divided into many subblocks, over which the statistical histograms are achieved independently. Finally, a Fisher linear discriminant (FLD) classifier is learned in the statistical histogram feature space for palmprint identification. Experiments are executed over the HongKong PolyU palmprint database of 7752 images. To verify the high performance of our proposed feature descriptor, several other multiresolution and multidirectional transforms are also investigated including Gabor filter, dual-tree complex wavelet and Contourlet transforms. The experimental results demonstrate that CDFB yields the most promising performance balancing the identification accuracy, storage requirement and computational complexity for our proposed feature extraction framework.